code: def create_distribution(space):
      """Creates a Distribution for the given Gym space."""
      if isinstance(space, gym.spaces.Discrete):
          return Categorical(shape=(), n_categories=space.n)
      elif isinstance(space, gym.spaces.MultiDiscrete):
          assert space.nvec.size
          assert min(space.nvec) == max(space.nvec), (
              "Every dimension must have the same number of categories, got "
              "{}.".format(space.nvec)
          )
          return Categorical(
              shape=(len(space.nvec),), n_categories=space.nvec[0]
          )
      elif isinstance(space, gym.spaces.Box):
          return Gaussian(shape=space.shape)
      else:
          raise TypeError(
              "Space {} unavailable as a distribution support."
          )
  
imports: import gin
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: create_distribution(space) and returns: Categorical, Categorical, Gaussian